A functional assessment of arterial capacity is important for treatment planning to address patient needs. Recent studies have demonstrated that hemodynamic characteristics, such as Fractional Flow Reserve (FFR), are important indicators to determine the optimal treatment for a patient with arterial disease. Conventional assessments of these hemodynamic characteristics use invasive catheterizations to directly measure blood flow characteristics, such as pressure and flow velocity. However, despite the important clinical information that is gathered, these invasive measurement techniques present severe risks to the patient and significant costs to the healthcare system.
To address the risks and costs associated with invasive measurement, a new generation of noninvasive tests have been developed to assess blood flow characteristics. These noninvasive tests use patient imaging (such as computed tomography (CT)) to determine a patient-specific geometric model of the blood vessels and this model is used computationally to simulate the blood flow using computational fluid dynamics (CFD) with appropriate physiological boundary conditions and parameters. Examples of inputs to these patient-specific boundary conditions include the patient's blood pressure, blood viscosity and the expected demand of blood from the supplied tissue (derived from scaling laws and a mass estimation of the supplied tissue from the patient imaging). Although these simulation-based estimations of blood flow characteristics have demonstrated a level of fidelity comparable to direct (invasive) measurements of the same quantity of interest, physical simulations demand a substantial computational burden that can make these virtual, noninvasive tests difficult to execute in a real-time clinical environment. Consequently, the present disclosure describes new approaches for performing rapid, noninvasive estimations of blood flow characteristics that are computationally inexpensive.